For the fusion of temporal and non-temporal features, many studies directly adopt the additive method, such as the most famous Transformer architecture [1]}. The fusion of temporal and non-temporal features is not a simple additive relationship, so the method shown in the Fig. REF  is proposed. Firstly, stack the temporal and non-temporal features, then increase the dimension of the two features used \(1 \times 1\)  convolution structure. After processing by the \(tanh\)  activation function, we eventually fuse features into one-dimensional tensor on another \(1 \times 1\)  convolution structure.
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